VeriLook SDK

Face identification for stand-alone or Web applications

VeriLook facial identification technology is designed for biometric systems developers and integrators.
The technology assures system performance and reliability with live face detection, simultaneous multiple face recognition and fast face matching in 1-to-1 and 1-to-many modes.

Available as a software development kit that allows development of stand-alone and Web-based solutions on Microsoft Windows, Linux, Mac OS X, iOS and Android platforms.

According to the original protocol, only 6,000 pairs (3,000 genuine and 3,000 impostor) should be used to report the results.
But recent algorithms are "very close to the maximum achievable by a perfect classifier" [source].
Instead, as Neurotechnology algorithms were not trained on any image from this dataset, verification results on matching each pair of all 13,233 face images of 5,729 persons were chosen to be reported.

All identity mistakes, which had been mentioned on the LFW website, were fixed.
Also, several not mentioned issues were fixed.

Some images from the LFW dataset contained multiple faces.
The correct faces for assigned identities were chosen manually to solve these ambiguities.

The dataset contains face images, which were captured in visible light (VIS) and near-infrared (NIR) spectrums.
According to the original protocol, VeriLook algorithm testing used VIS images as gallery, and NIR images as probe.

According to the original protocol, the dataset is split into two parts – View1 intended for algorithm development and View2 for performance evaluation.
Neurotechnology algorithms were not trained on any image from this dataset.
Only View2 part with 12,393 NIR images and 2,564 VIS images was used for face verification evaluation.

The non-cropped images (640 x 480 pixels) from the dataset were used for VeriLook algorithm testing.

Receiver operation characteristic (ROC) curves are usually used to demonstrate the recognition quality of an algorithm.
ROC curves show the dependence of false rejection rate (FRR) on the false acceptance rate (FAR).
Equal error rate (EER) is the rate at which both FAR and FRR are equal.